Skip to main content
TensorPool offers a variety of GPU instance types that can be used for both clusters and jobs.
Instance Type
1xB300
8xB300
1xB200
2xB200
4xB200
8xB200
1xH200
2xH200
4xH200
8xH200
1xH100
2xH100
4xH100
8xH100
1xMI300X
2xMI300X
4xMI300X

​
Where to Use Instance Types

  • Clusters: Long-running infrastructure for development, training, and experimentation
  • Jobs: Single-run compute tasks with automatic cleanup after completion

​
Choosing the Right Instance Type

​
Single GPU (1x)

Best for development, debugging, small models, and inference. Most cost-effective for getting started.

​
Multi-GPU Single Node (2x, 4x, 8x)

For training larger models with data parallelism or model parallelism

​
Multi-Node Clusters

The following instance are available in multi-node clusters:
  • 8xB200
  • 8xH200

​
Examples

​
Creating Clusters

# Single H200 node
tp cluster create -t 1xB200

# Multi-node: 4 nodes with 8xB200 each (32 GPUs total)
tp cluster create -t 8xB200 -n 4

​
Running Jobs

# tp.config.toml - Single H100 job
commands = ["python train.py"]
instance_type = "1xH100"
outputs = ["checkpoints/"]
ignore = [".git", ".venv"]

tp job push tp.config.toml

​
Next Steps